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The Lead Data Engineer is responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem across two independent health plan technology stacks. This role owns the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization), and improves the reliability, observability, and security posture of data pipelines supporting critical Medicaid operations.
The Lead Data Engineer partners closely with the Market BI team as their IT counterpart to improve data access and flow, including establishing and managing Databricks pipeline patterns and platform enablement as the BI environment evolves.

The Lead Data Engineer owns and evolves the Wisconsin Medicaid Market's data stores and data movement ecosystem, including the Data Warehouse and ODS, and the ETL processes that connect vendor and internal systems. This role is accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex dual-stack environment.

The Lead Data Engineer is also the Market BI team's primary IT partner for data platform enablement — improving data access patterns, strengthening pipeline governance, and helping establish a maintainable approach to Databricks pipelines and workflows as the market matures its analytics capabilities.

This is a Lead-level individual contributor role where work requires higher autonomy and complexity and includes directing the work of a small number of contract resources while remaining hands-on in delivery.

Team Culture & Expectations

On the Wisconsin Medicaid Market IT team, how you show up is as important as what you accomplish. We value a positive attitude, curiosity, ownership, and a desire to learn and make a meaningful difference. This role requires collaboration, high accountability, and comfort operating in ambiguity while continuously improving data reliability, security, and outcomes for associates, members, providers, and other stakeholders.

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Skills & Capabilities

The ideal candidate blends deep data engineering fundamentals with the ability to lead through influence — setting direction, improving reliability, and guiding others' work (including contract resources) while staying hands-on.

Required Skills

• SQL, ETL/ELT & Data Platform Depth - Proven production experience with SQL Server data engineering, including warehousing patterns, operational support, performance tuning, and ETL/ELT design. Strong experience with SSIS/SSRS and Azure Data Factory (or similar orchestration) in real operating environments.
• Integration & Interoperability Tooling (WI Market Reality) - Experience working across integration engines and healthcare data movement patterns, including tools such as Rhapsody / CorePoint
• Modernization, Optimization & Stabilization Mindset - Demonstrated ability to modernize and optimize fragile pipelines and legacy patterns, reduce technical debt, and improve reliability, observability, and fault tolerance.
• Databricks / UDAP Growth Path (directional, not gatekeeping) - Experience with Databricks and/or enterprise data platforms (e.g., UDAP), or strong aptitude and desire to grow into these platforms as part of market modernization and enterprise alignment.
• Execution Leadership - Highly organized, self-directed, and able to drive work to outcomes in an ambiguous, rapidly changing environment including planning, sequencing, and communicating progress/risk. Experience creating detailed technical documentation to gain buy-in and drive decisions. Team operates in SAFe Agile practices.
• Vendor / Contract Resource Direction - Ability to direct and review contract resource work (clarify requirements, establish standards, review deliverables, ensure maintainability).

Preferred Skills

• Own the market's data stores (Data Warehouse + ODS) and ensure stability, security, and performance
• AI Integration
• Lead modernization and optimization of the ETL ecosystem, including SSIS modernization and improved reliability/observability
• Drive reduction of legacy report patterns and support transition aligned to enterprise SSRS footprint reduction efforts
• Partner with Market BI as their IT counterpart to improve data access and establish/manage Databricks pipeline patterns
• Define and enforce data engineering standards aligned with enterprise direction.
• Reduce technical debt and streamline workflows to lower operational burden on engineers and increas